






"""simple docstring"""



from __future__ import annotations

from typing import Any





class    _UpperCAmelCase			(					__snake_case				):
					pass





class    _UpperCAmelCase			:




					def __init__(							self	:		Optional[int]       ,		A	:		Any  )    ->    None:
							lowercase_					:					List[Any]							=  data
							lowercase_					:					List[Any]							=  None




					def __iter__(							self	:		List[Any]  )    ->    str:
							lowercase_					:					Tuple							=  self
							lowercase_					:					int							=  []
							while node:
									if node in visited:
											raise ContainsLoopError
									visited.append(__SCREAMING_SNAKE_CASE  )
									yield node.data
									lowercase_					:					List[str]							=  node.next_node






					@property
					def     A    (							self	:		Any  )    ->    bool:
							try:
									list(self  )
									return False
							except ContainsLoopError:
									return True


if __name__ == "__main__":
						__A						:			Any    							=						Node(1)
						__A						:			int    							=						Node(2)
						__A						:			Union[str, Any]    							=						Node(3)
						__A						:			Dict    							=						Node(4)
						print(root_node.has_loop)  # False
						__A						:			List[str]    							=						root_node.next_node
						print(root_node.has_loop)  # True

						__A						:			List[str]    							=						Node(5)
						__A						:			Union[str, Any]    							=						Node(6)
						__A						:			Union[str, Any]    							=						Node(5)
						__A						:			Optional[int]    							=						Node(6)
						print(root_node.has_loop)  # False

						__A						:			Optional[int]    							=						Node(1)
						print(root_node.has_loop)  # False











"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class 	a		(   __snake_case    , unittest.TestCase						):
    SCREAMING_SNAKE_CASE		:	Optional[Any]		=					KandinskyImgaImgPipeline
    SCREAMING_SNAKE_CASE		:	Tuple		=					["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
    SCREAMING_SNAKE_CASE		:	int		=					[
        """prompt""",
        """negative_prompt""",
        """image_embeds""",
        """negative_image_embeds""",
        """image""",
    ]
    SCREAMING_SNAKE_CASE		:	List[str]		=					[
        """generator""",
        """height""",
        """width""",
        """strength""",
        """guidance_scale""",
        """negative_prompt""",
        """num_inference_steps""",
        """return_dict""",
        """guidance_scale""",
        """num_images_per_prompt""",
        """output_type""",
        """return_dict""",
    ]
    SCREAMING_SNAKE_CASE		:	Any		=					False






    @property
    def   UpperCamelCase			(			self	:				List[str]   )	->						str:
      return 32






    @property
    def   UpperCamelCase			(			self	:				Tuple   )	->						Optional[Any]:
      return 32






    @property
    def   UpperCamelCase			(			self	:				Optional[int]   )	->						str:
      return self.time_input_dim






    @property
    def   UpperCamelCase			(			self	:				Optional[int]   )	->						Optional[Any]:
      return self.time_input_dim * 4






    @property
    def   UpperCamelCase			(			self	:				int   )	->						List[Any]:
      return 100






    @property
    def   UpperCamelCase			(			self	:				Any   )	->						List[Any]:
      lowerCamelCase_       	=		XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'   )
      return tokenizer






    @property
    def   UpperCamelCase			(			self	:				str   )	->						Optional[Any]:
      torch.manual_seed(0   )
      lowerCamelCase_       	=		MCLIPConfig(
          numDims=self.cross_attention_dim			,		transformerDimensions=self.text_embedder_hidden_size			,		hidden_size=self.text_embedder_hidden_size			,		intermediate_size=37			,		num_attention_heads=4			,		num_hidden_layers=5			,		vocab_size=1005			,		)

      lowerCamelCase_       	=		MultilingualCLIP(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		text_encoder.eval()

      return text_encoder






    @property
    def   UpperCamelCase			(			self	:				int   )	->						List[Any]:
      torch.manual_seed(0   )

      lowerCamelCase_       	=		{
          'in_channels': 4,
          # Out channels is double in channels because predicts mean and variance
          'out_channels': 8,
          'addition_embed_type': 'text_image',
          'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
          'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
          'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
          'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
          'layers_per_block': 1,
          'encoder_hid_dim': self.text_embedder_hidden_size,
          'encoder_hid_dim_type': 'text_image_proj',
          'cross_attention_dim': self.cross_attention_dim,
          'attention_head_dim': 4,
          'resnet_time_scale_shift': 'scale_shift',
          'class_embed_type': None,
      }

      lowerCamelCase_       	=		UNetaDConditionModel(**__SCREAMING_SNAKE_CASE   )
      return model






    @property
    def   UpperCamelCase			(			self	:				Tuple   )	->						Optional[Any]:
      return {
          "block_out_channels": [32, 64],
          "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
          "in_channels": 3,
          "latent_channels": 4,
          "layers_per_block": 1,
          "norm_num_groups": 8,
          "norm_type": "spatial",
          "num_vq_embeddings": 12,
          "out_channels": 3,
          "up_block_types": [
              "AttnUpDecoderBlock2D",
              "UpDecoderBlock2D",
          ],
          "vq_embed_dim": 4,
      }






    @property
    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						Optional[Any]:
      torch.manual_seed(0   )
      lowerCamelCase_       	=		VQModel(**self.dummy_movq_kwargs   )
      return model






    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						List[Any]:
      lowerCamelCase_       	=		self.dummy_text_encoder
      lowerCamelCase_       	=		self.dummy_tokenizer
      lowerCamelCase_       	=		self.dummy_unet
      lowerCamelCase_       	=		self.dummy_movq

      lowerCamelCase_       	=		{
          'num_train_timesteps': 1000,
          'beta_schedule': 'linear',
          'beta_start': 0.00_085,
          'beta_end': 0.012,
          'clip_sample': False,
          'set_alpha_to_one': False,
          'steps_offset': 0,
          'prediction_type': 'epsilon',
          'thresholding': False,
      }

      lowerCamelCase_       	=		DDIMScheduler(**__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		{
          'text_encoder': text_encoder,
          'tokenizer': tokenizer,
          'unet': unet,
          'scheduler': scheduler,
          'movq': movq,
      }

      return components






    def   UpperCamelCase			(			self	:				Dict			,		__SCREAMING_SNAKE_CASE	:				int			,		__SCREAMING_SNAKE_CASE	:				Dict=0   )	->						str:
      lowerCamelCase_       	=		floats_tensor((1, self.cross_attention_dim)			,		rng=random.Random(__SCREAMING_SNAKE_CASE   )   ).to(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		floats_tensor((1, self.cross_attention_dim)			,		rng=random.Random(seed + 1   )   ).to(__SCREAMING_SNAKE_CASE   )
      # create init_image
      lowerCamelCase_       	=		floats_tensor((1, 3, 64, 64)			,		rng=random.Random(__SCREAMING_SNAKE_CASE   )   ).to(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		image.cpu().permute(0			,		2			,		3			,		1   )[0]
      lowerCamelCase_       	=		Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE   )   ).convert('RGB'   ).resize((256, 256)   )

      if str(__SCREAMING_SNAKE_CASE   ).startswith('mps'   ):
        lowerCamelCase_       	=		torch.manual_seed(__SCREAMING_SNAKE_CASE   )
      else:
        lowerCamelCase_       	=		torch.Generator(device=__SCREAMING_SNAKE_CASE   ).manual_seed(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		{
          'prompt': 'horse',
          'image': init_image,
          'image_embeds': image_embeds,
          'negative_image_embeds': negative_image_embeds,
          'generator': generator,
          'height': 64,
          'width': 64,
          'num_inference_steps': 10,
          'guidance_scale': 7.0,
          'strength': 0.2,
          'output_type': 'np',
      }
      return inputs






    def   UpperCamelCase			(			self	:				Tuple   )	->						Any:
      lowerCamelCase_       	=		'cpu'

      lowerCamelCase_       	=		self.get_dummy_components()

      lowerCamelCase_       	=		self.pipeline_class(**__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		pipe.to(__SCREAMING_SNAKE_CASE   )

      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		pipe(**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE   )   )
      lowerCamelCase_       	=		output.images

      lowerCamelCase_       	=		pipe(
          **self.get_dummy_inputs(__SCREAMING_SNAKE_CASE   )			,		return_dict=__SCREAMING_SNAKE_CASE			,		)[0]

      lowerCamelCase_       	=		image[0, -3:, -3:, -1]
      lowerCamelCase_       	=		image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 64, 64, 3)

      lowerCamelCase_       	=		np.array(
          [0.61_474_943, 0.6_073_539, 0.43_308_544, 0.5_928_269, 0.47_493_595, 0.46_755_973, 0.4_613_838, 0.45_368_797, 0.50_119_233]   )
      assert (
          np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
      ), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
      assert (
          np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1e-2
      ), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''






@slow
@require_torch_gpu
class 	a		(   unittest.TestCase						):






    def   UpperCamelCase			(			self	:				Tuple   )	->						List[str]:
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()






    def   UpperCamelCase			(			self	:				Any   )	->						int:
      lowerCamelCase_       	=		load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/kandinsky/kandinsky_img2img_frog.npy'   )

      lowerCamelCase_       	=		load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'   )
      lowerCamelCase_       	=		'A red cartoon frog, 4k'

      lowerCamelCase_       	=		KandinskyPriorPipeline.from_pretrained(
          'kandinsky-community/kandinsky-2-1-prior'			,		torch_dtype=torch.floataa   )
      pipe_prior.to(__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		KandinskyImgaImgPipeline.from_pretrained(
          'kandinsky-community/kandinsky-2-1'			,		torch_dtype=torch.floataa   )
      lowerCamelCase_       	=		pipeline.to(__SCREAMING_SNAKE_CASE   )

      pipeline.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		torch.Generator(device='cpu'   ).manual_seed(0   )
      lowerCamelCase_	,      lowerCamelCase_       	=		pipe_prior(
          __SCREAMING_SNAKE_CASE			,		generator=__SCREAMING_SNAKE_CASE			,		num_inference_steps=5			,		negative_prompt=''			,		).to_tuple()

      lowerCamelCase_       	=		pipeline(
          __SCREAMING_SNAKE_CASE			,		image=__SCREAMING_SNAKE_CASE			,		image_embeds=__SCREAMING_SNAKE_CASE			,		negative_image_embeds=__SCREAMING_SNAKE_CASE			,		generator=__SCREAMING_SNAKE_CASE			,		num_inference_steps=100			,		height=768			,		width=768			,		strength=0.2			,		output_type='np'			,		)

      lowerCamelCase_       	=		output.images[0]

      assert image.shape == (768, 768, 3)

      assert_mean_pixel_difference(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )




